This chapter starts with an introduction to the CMC task (Section REF ). Using the CoQA [1]} and QuAC [2]} benchmarks as examples, we discuss the dialog features, such as topic shift, drill down, clarification, which the CMC module needs to handle.
Then, we describe two categories of machine comprehension models, known as extractive and generative readers, originally developed for sing-turn QA tasks.
While extractive readers (Section REF ) select a text span in the retrieved documents as the answer to the user query,
generative readers (Section REF ) can generate answer strings which might not be contained in the retrieved documents.
These two types of readers can be combined to produce better QA results (Section REF ).
We then examine approaches to extending these readers to use conversational context for CMC (Section REF ).
